package com.jspx.graphics;

/**
 * Created by yuan on 2015/7/1 0001.
 */
import java.awt.Graphics2D;
import java.awt.color.ColorSpace;
import java.awt.image.BufferedImage;
import java.awt.image.ColorConvertOp;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.InputStream;

import javax.imageio.ImageIO;
/*
* pHash-like image hash.
* Author: Elliot Shepherd (elliot@jarofworms.com<script cf-hash="f9e31" type="text/javascript">
<![CDATA[!function(){try{var t="currentScript"in document?document.currentScript:function(){for(var t=document.getElementsByTagName("script"),e=t.length;e--;)if(t[e].getAttribute("cf-hash"))return t[e]}();if(t&&t.previousSibling){var e,r,n,i,c=t.previousSibling,a=c.getAttribute("data-cfemail");if(a){for(e="",r=parseInt(a.substr(0,2),16),n=2;a.length-n;n+=2)i=parseInt(a.substr(n,2),16)^r,e+=String.fromCharCode(i);e=document.createTextNode(e),c.parentNode.replaceChild(e,c)}}}catch(u){}}();/* ]]>
         Based On: http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html

图片相似度比较算法  pHash
可以作为 指纹识别

结果说明：汉明距离越大表明图片差异越大，如果不相同的数据位不超过5，就说明两张图片很相似；如果大于10，就说明这是两张不同的图片。从结果可以看到1、2、3是相似图片，4、5差异太大，是两张不同的图片。
* */
public class ImagePHash {

    private int size = 32;
    private int smallerSize = 8;

    public ImagePHash() {
        initCoefficients();
    }

    public ImagePHash(int size, int smallerSize) {
        this.size = size;
        this.smallerSize = smallerSize;

        initCoefficients();
    }

    public int distance(String s1, String s2) {
        int counter = 0;
        for (int k = 0; k < s1.length();k++) {
            if(s1.charAt(k) != s2.charAt(k)) {
                counter++;
            }
        }
        return counter;
    }

    // Returns a 'binary string' (like. 001010111011100010) which is easy to do a hamming distance on.
    public String getHash(InputStream is) throws Exception {
        BufferedImage img = ImageIO.read(is);

       /* 1. Reduce size.
        * Like Average Hash, pHash starts with a small image.
        * However, the image is larger than 8x8; 32x32 is a good size.
        * This is really done to simplify the DCT computation and not
        * because it is needed to reduce the high frequencies.
        */
        img = resize(img, size, size);

       /* 2. Reduce color.
        * The image is reduced to a grayscale just to further simplify
        * the number of computations.
        */
        img = grayscale(img);

        double[][] vals = new double[size][size];

        for (int x = 0; x < img.getWidth(); x++) {
            for (int y = 0; y < img.getHeight(); y++) {
                vals[x][y] = getBlue(img, x, y);
            }
        }

       /* 3. Compute the DCT.
        * The DCT separates the image into a collection of frequencies
        * and scalars. While JPEG uses an 8x8 DCT, this algorithm uses
        * a 32x32 DCT.
        */
        long start = System.currentTimeMillis();
        double[][] dctVals = applyDCT(vals);
        System.out.println("DCT: " + (System.currentTimeMillis() - start));

       /* 4. Reduce the DCT.
        * This is the magic step. While the DCT is 32x32, just keep the
        * top-left 8x8. Those represent the lowest frequencies in the
        * picture.
        */
       /* 5. Compute the average value.
        * Like the Average Hash, compute the mean DCT value (using only
        * the 8x8 DCT low-frequency values and excluding the first term
        * since the DC coefficient can be significantly different from
        * the other values and will throw off the average).
        */
        double total = 0;

        for (int x = 0; x < smallerSize; x++) {
            for (int y = 0; y < smallerSize; y++) {
                total += dctVals[x][y];
            }
        }
        total -= dctVals[0][0];

        double avg = total / (double) ((smallerSize * smallerSize) - 1);

       /* 6. Further reduce the DCT.
        * This is the magic step. Set the 64 hash bits to 0 or 1
        * depending on whether each of the 64 DCT values is above or
        * below the average value. The result doesn't tell us the
        * actual low frequencies; it just tells us the very-rough
        * relative scale of the frequencies to the mean. The result
        * will not vary as long as the overall structure of the image
        * remains the same; this can survive gamma and color histogram
        * adjustments without a problem.
        */
        String hash = "";

        for (int x = 0; x < smallerSize; x++) {
            for (int y = 0; y < smallerSize; y++) {
                if (x != 0 && y != 0) {
                    hash += (dctVals[x][y] > avg?"1":"0");
                }
            }
        }

        return hash;
    }

    private BufferedImage resize(BufferedImage image, int width,    int height) {
        BufferedImage resizedImage = new BufferedImage(width, height, BufferedImage.TYPE_INT_ARGB);
        Graphics2D g = resizedImage.createGraphics();
        g.drawImage(image, 0, 0, width, height, null);
        g.dispose();
        return resizedImage;
    }

    private ColorConvertOp colorConvert = new ColorConvertOp(ColorSpace.getInstance(ColorSpace.CS_GRAY), null);

    private BufferedImage grayscale(BufferedImage img) {
        colorConvert.filter(img, img);
        return img;
    }

    private static int getBlue(BufferedImage img, int x, int y) {
        return (img.getRGB(x, y)) & 0xff;
    }

    // DCT function stolen from http://stackoverflow.com/questions/4240490/problems-with-dct-and-idct-algorithm-in-java

    private double[] c;
    private void initCoefficients() {
        c = new double[size];

        for (int i=1;i<size;i++) {
            c[i]=1;
        }
        c[0]=1/Math.sqrt(2.0);
    }

    private double[][] applyDCT(double[][] f) {
        int N = size;

        double[][] F = new double[N][N];
        for (int u=0;u<N;u++) {
            for (int v=0;v<N;v++) {
                double sum = 0.0;
                for (int i=0;i<N;i++) {
                    for (int j=0;j<N;j++) {
                        sum+=Math.cos(((2*i+1)/(2.0*N))*u*Math.PI)*Math.cos(((2*j+1)/(2.0*N))*v*Math.PI)*(f[i][j]);
                    }
                }
                sum*=((c[u]*c[v])/4.0);
                F[u][v] = sum;
            }
        }
        return F;
    }

    public static void main(String[] args) {

        ImagePHash p = new ImagePHash();
        String image1;
        String image2;
        try {
            image1 = p.getHash(new FileInputStream(new File("C:/Users/june/Desktop/1.jpg")));
            image2 = p.getHash(new FileInputStream(new File("C:/Users/june/Desktop/1.jpg")));
            System.out.println("1:1 Score is " + p.distance(image1, image2));
            image1 = p.getHash(new FileInputStream(new File("C:/Users/june/Desktop/1.jpg")));
            image2 = p.getHash(new FileInputStream(new File("C:/Users/june/Desktop/2.jpg")));
            System.out.println("1:2 Score is " + p.distance(image1, image2));
            image1 = p.getHash(new FileInputStream(new File("C:/Users/june/Desktop/1.jpg")));
            image2 = p.getHash(new FileInputStream(new File("C:/Users/june/Desktop/3.jpg")));
            System.out.println("1:3 Score is " + p.distance(image1, image2));
            image1 = p.getHash(new FileInputStream(new File("C:/Users/june/Desktop/2.jpg")));
            image2 = p.getHash(new FileInputStream(new File("C:/Users/june/Desktop/3.jpg")));
            System.out.println("2:3 Score is " + p.distance(image1, image2));

            image1 = p.getHash(new FileInputStream(new File("C:/Users/june/Desktop/4.jpg")));
            image2 = p.getHash(new FileInputStream(new File("C:/Users/june/Desktop/5.jpg")));
            System.out.println("4:5 Score is " + p.distance(image1, image2));

        } catch (FileNotFoundException e) {
            e.printStackTrace();
        } catch (Exception e) {
            e.printStackTrace();
        }

    }
}